BraGCL-Brain Graph Contrastive Learning Framework

The paper "An Interpretable Brain Graph Contrastive Learning Framework for Brain Disorder Analysis" has accepted by WSDM 2024 Demo

Introduction

We propose an interpretable brain graph contrastive learning framework that aims to learn brain graph representations from insufficient label data for disorder prediction and pathogenic analysis. Our framework consists of two key designs: We first use the controllable data augmentation strategy to perturb unimportant structures and attribute features according to node and edge importance scores. Then, considering that the difference between healthy control and patient brain graphs is small, we introduce hard negative sample evaluation to weight negative samples of the contrastive loss, which can learn more discriminative brain graph representations. More importantly, our method can observe salient brain regions and connections to explain prediction results. We conducted disorder prediction and interpretable analysis experiments on three publicly available neuroimaging datasets to demonstrate the effectiveness of our framework.

Framework

Implementation

Brain Graph Construction

Given the neuroimaging of one brain disorder, we first preprocess these neuroimaging according to the process described by [BrainGB]. Then, we generate the connectivity matrix, such as the functional connectivity matrix of fMRI and the structural connectivity matrix of DTI. Finally, we construct the adjacent matrix via the K-NN algorithm.

dataset

In this project, three datasets have been evaluated, including HIV, BP and ABIDE.

• HIV and BP are private datasets that can not be accessed without authorization.
• ABIDE is a public dataset that could be downloaded from [Nilearn]

Pipeline

1.Data Preparation

The input of neuroimaging data, such as fMRI and DTI, is first preprocessed and they are modeled into brain graphs based on the above construction process.

2.Model Training

BraGCL first performs the controllable data augmentation strategy to perturb unimportant structures and attribute features according to node and edge importance scores, so that the input brain graph will be generated two different views of brain graphs. Secondly, BraGCL conducts the optimization of contrastive loss based on hard negative samples weight to enhance the ability of discriminative brain graph representation learning.

3.Model Evaluation

After the unsupervised model pre-training above, we utilize brain graph representations obtained by the pre-trained model above to predict disorder and identify salient brain regions and connections.

Experiment

Performance Evaluation

The proposed method(BraGCL) outperforms all baseline methods on three datasets.

Note:

• PreGCN is designed by us to contrast the performance of our framework with the pre-training model. It contains three components: 1 MLP layer(unified input dimension of GNN), 2-GCN layers, and 1-MLP layer(classification). PPMI has been used to pre-train the model, and HIV, BP, and ABIDE are used to fine-tune the model respectively.
• PPMI could be downloaded from [PPMI]

Brain Disorder Analysis

Visualizing salient brain regions and connections for different brain disorders.

Hyperparameter Definition

1. Hyperparameters $\epsilon, \gamma$ are computed by the distributions of edge and node importance in the brain graph, as follows: $\epsilon= \mu{s} + \lambda{s} ∗ \sigma{s}$, where $\mu{s}$ and $\sigma{s}$ are the mean value and standard deviation of all edge scores from a graph. $\lambda{s}$ is the hyperparameter to control the size of important part, because the range of importance scores can be different for different datasets.
2. Similarly, $\gamma= \mu{f} + \lambda{f} ∗ \sigma{f}$, where $\mu{f}$ and $\sigma_{f}$ are the mean value and standard deviation of all node scores from a graph.
3. $\beta=1/\mu{w}$, where $\mu{w}$ is the mean value of hardness $\varphi$.

   Running

   Requirement

   The framework needs the following dependencies:

   torch~=1.10.2
   numpy~=1.22.2
   scikit-learn~=1.0.2
   scipy~=1.7.3
   pandas~=1.4.1
   tqdm~=4.62.3
   torch-geometric~=2.0.3
   torch-cluster 1.5.9
   faiss-cpu 1.7.2

   Run

   To run our model on any of the datasets in our paper, simply run:

   python main.py --dataset =<dataset name> --modality=<fmri/dti>

   --dataset is the name of the dataset(HIV, BP are private datasets, ABIDE is a public dataset that could be used for everyone)
   --modality is the type of data, selecting from fmri and dti
   Please place the dataset files in the data/ folder under the root folder